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Investigating Pill Recognition Methods for a New National Library of Medicine Image Dataset

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Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9475))

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Abstract

With the increasing access to pharmaceuticals, chances are that medication administration errors will occur more frequently. On average, individuals above age 65 take at least 14 prescriptions per year. Unfortunately, adverse drug reactions and noncompliance are responsible for 28 % of hospitalizations of the elderly. Correctly identifying pills has become a critical task in patient care and safety. Using the recently released National Library of Medicine (NLM) pill image database, this paper investigates descriptors for pill detection and characterization. We describe efforts in investigating algorithms to segment NLM pills images automatically, and extract several features to assembly pill groups with priors based on FDA recommendations for pill physical attributes. Our contributions toward pill recognition automation are three-fold: we evaluate the 1,000 most common medications in the United States, provide masks and feature matrices for the NLM reference pill images to guarantee reproducibility of results, and discuss strategies to organize data for efficient content-based image retrieval.

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References

  1. Hale, D.: Pill meets poka to enhance patient safety. Medicine 2.0 Congress (2009)

    Google Scholar 

  2. Fiji: Fiji is just imagej. (http://fiji.sc/Fiji). Accessed 27 August 2015

  3. NIH NLM: Pillbox rapid identification, reliable information. (http://pillbox.nlm.nih.gov/pillimage/search.php). Accessed 27 August 2015

  4. WebMD: Pill identification tool. (http://www.webmd.com/pill-identification/). Accessed 27 August 2015

  5. Caban, J., Rosebrock, A., Yoo, T.: Automatic identification of prescription drugs using shape distribution models. In: ICIP, pp. 1005–1008 (2012)

    Google Scholar 

  6. Lee, Y.B., Park, U., Jain, A.K., Lee, S.W.: Pill-id: matching and retrieval of drug pill images. Pattern Recogn. Lett. 33, 904–910 (2012)

    Article  Google Scholar 

  7. Lee, H.C., Chang, T.: Pill recognition using image processing and neural networks. Gerontechnology 13, 335 (2014)

    Google Scholar 

  8. Yu, J., Chen, Z., Kamata, S.I.: Pill recognition using imprint information by two-step sampling distance sets. In: International Conference on Pattern Recognition, pp. 3156–3161 (2014)

    Google Scholar 

  9. FDA: Size, shape, and other physical attributes of generic tablets and capsules guidance for industry. (http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm377938.pdf). Accessed 27 August 2015

  10. Fiji-plugins: Analysis tools. (http://rsb.info.nih.gov/ij/docs/menus/analyze.html). Accessed 27 August 2015

  11. Hartigan, J.A., Wong, M.A.: A k-means clustering algorithm. Appl. Stat. 28, 100–108 (1979)

    Article  MATH  Google Scholar 

  12. Kohonen, T., Schroeder, M.R., Huang, T.S. (eds.): Self-Organizing Maps, 3rd edn. Springer-Verlag New York Inc., Secaucus (2001)

    MATH  Google Scholar 

  13. Strong, G., Gong, M.: Similarity-based image organization and browsing using multi-resolution self-organizing map. Image Vis. Comput. 29, 774–786 (2011)

    Article  Google Scholar 

  14. Kuroiwa, J., Inawashiro, S., Miyake, S., Aso, H.: Self-organization of orientation maps in a formal neuron model using a cluster learning rule. Neural Netw. 13, 31–40 (2000)

    Article  Google Scholar 

  15. Rousseeuw, P.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)

    Article  MATH  Google Scholar 

  16. de Amorim, R.C., Hennig, C.: Recovering the number of clusters in data sets with noise features using feature rescaling factors. Inf. Sci. 324, 126–145 (2015)

    Article  Google Scholar 

  17. Backhaus, A., Kuwabara, A., Bauch, M., Monk, N., Sanguinetti, G., Fleming, A.: LEAFPROCESSOR: a new leaf phenotyping tool using contour bending energy and shape cluster analysis. New Phytol. 187, 251–261 (2010)

    Article  Google Scholar 

  18. Vinh, N.X., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J. Mach. Learn. Res. 11, 2837–2854 (2010)

    MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. BIDS, University of California, Berkeley, CA, 94720, USA

    Daniela Ushizima

  2. CRD, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA

    Daniela Ushizima

  3. LABVIS, Federal University of Ceara, Fortaleza, CE, 60020-181, Brazil

    Allan Carneiro, Marcelo Souza & Fatima Medeiros

Authors
  1. Daniela Ushizima
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  2. Allan Carneiro
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  3. Marcelo Souza
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  4. Fatima Medeiros
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Corresponding author

Correspondence to Daniela Ushizima .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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© 2015 Springer International Publishing Switzerland

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Ushizima, D., Carneiro, A., Souza, M., Medeiros, F. (2015). Investigating Pill Recognition Methods for a New National Library of Medicine Image Dataset. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_38

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  • DOI: https://doi.org/10.1007/978-3-319-27863-6_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27862-9

  • Online ISBN: 978-3-319-27863-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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